Better models don't come from more tokens — they come from better data. Wisfer takes you from raw industry datasets to a measurably improved model through a transparent, benchmark-driven process. Here's exactly how we help you train.
Start from your industry in focus. Browse Wisfer's marketplace of human-verified intelligence datasets and pick the ones aligned to your domain, use case, and quality tier.
Shape the data to your objective — filter by signal, region, and freshness; merge sources; apply privacy rules and remove bias before a single training run.
Dial in learning rate, batch size, epochs, and adapter strategy (LoRA, QLoRA, or full-parameter). Sensible defaults per model family get you started fast.
Establish a baseline. We score your current model and data across ten dimensions — accuracy, coverage, bias, freshness, and more — so improvement is measurable, not anecdotal.
Run managed training on the enriched datasets, then automatically re-score against the same benchmark suite for a true apples-to-apples comparison.
A clear before/after report shows exactly where the model improved — which metrics moved, by how much, and which datasets drove the gains.
Prune underperforming data, add fresh signals, and re-tune. Each cycle compounds — turning a one-off run into a continuously improving intelligence loop.
A real benchmark run — baseline model against the same model trained on Wisfer datasets.
Walkthroughs and results from models recently trained on the platform.
We swapped in Wisfer's human-intelligence datasets and re-trained in a weekend. The benchmark report made the accuracy jump impossible to argue with.+16% accuracy
The before/after benchmarking is the part our compliance team loved — every improvement was measured, versioned, and explainable.−38% hallucinations
Fine-tuning that used to take weeks now closes in a day. The continuous optimization loop keeps our model fresh without a dedicated ops team.3× faster training